Improved fragment-based movement with LRFragLib for all-atom Ab initio protein folding

TL;DR

This paper introduces 'fragmove', an improved fragment-based movement strategy for all-atom ab initio protein folding, which enhances sampling efficiency and model accuracy by utilizing a logistic regression-based fragment library.

Contribution

The paper presents a novel movement method 'fragmove' derived from LRFragLib, significantly improving folding accuracy and energy minimization in protein simulations.

Findings

Increased secondary structure prediction accuracy by 11.24%.

Enhanced tertiary structure accuracy by 17.98%.

Reduced energy minima by 5.72%.

Abstract

Fragment-based assembly has been widely used in Ab initio protein folding simulation which can effectively reduce the conformational space and thus accelerate sampling. The efficiency of fragment-based movement as well as the quality of fragment library determine whether the folding process can lead the free energy landscape to the global minimum and help the protein to reach near-native folded state. We designed an improved fragment-based movement, "fragmove", which substituted multiple backbone dihedral angles in every simulation step. This movement strategy was derived from the fragment library generated by LRFragLib, an effective fragment detection algorithm using logistic regression model. We show in replica exchange Monte Carlo (REMC) simulation that "fragmove", when compared with a set of existing movements in REMC, shows significant improved ability at increasing secondary and tertiary predicted model accuracy by 11.24% and 17.98%, respectively and reaching energy minima decreased by 5.72%. Our results demonstrate that this improved movement is more powerful to guide proteins faster to low energy regions of conformational space and promote folding efficiency and predicted model accuracy.